# coding=utf-8
# Copyright 2024 The Google Research Authors.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

r"""Ground truth values for `german_credit_numeric_with_test_probit_regression`."""

import numpy as np

PARAMS_MEAN: np.ndarray = np.array([
    -0.4522559429246667,
    0.22805394831278347,
    -0.28982382009226004,
    0.08770205078549935,
    -0.21894166605129897,
    -0.1247236037780102,
    -0.031191677084684722,
    -0.01933512191470786,
    0.12926917363476775,
    -0.05232789717343509,
    -0.14157455896217294,
    0.10939460457060075,
    0.0019019487920089993,
    -0.1433137854310583,
    -0.20184024923103028,
    0.14554003104139893,
    -0.19364236850657068,
    0.17060463211465993,
    0.19585449865075236,
    -0.0043966611378192895,
    -0.09994386858595652,
    -0.04316062218796216,
    0.0026134313804582403,
    0.007173986133513828,
    -0.7234846148526666,
]).reshape((25,))

PARAMS_MEAN_STANDARD_ERROR: np.ndarray = np.array([
    3.945339418969067e-05,
    5.264727482933675e-05,
    4.4279921640975994e-05,
    5.588125445424163e-05,
    4.01604469043967e-05,
    4.1769873488768566e-05,
    3.580497911989816e-05,
    4.2284659428714837e-05,
    5.1354730786400655e-05,
    4.4619946306444976e-05,
    3.488448573392108e-05,
    4.389582122216544e-05,
    3.814412045033827e-05,
    4.484492888665821e-05,
    5.0471067702790895e-05,
    3.6872650582620364e-05,
    4.73968741603168e-05,
    5.791207728517237e-05,
    5.4338464766385175e-05,
    7.645013212335878e-05,
    7.815524129646374e-05,
    4.4257089314064174e-05,
    6.808624350235734e-05,
    6.548222794441334e-05,
    4.0519918136492984e-05,
]).reshape((25,))

PARAMS_STANDARD_DEVIATION: np.ndarray = np.array([
    0.059645463616306515,
    0.0724395379660803,
    0.06400014941127437,
    0.0753238568217787,
    0.061790421803763776,
    0.06207985067622209,
    0.055740366775277626,
    0.0633535350104843,
    0.0703364365711379,
    0.06463353733450458,
    0.053356506538735346,
    0.06349304413872847,
    0.05864695444578447,
    0.06496872796529975,
    0.07571981489903776,
    0.05657799661422822,
    0.06992103338575688,
    0.07713792431437279,
    0.07280638165568629,
    0.09554100246519945,
    0.09769874636219475,
    0.06348253321827296,
    0.08728496976307365,
    0.0838511039270485,
    0.059992579892849264,
]).reshape((25,))

PER_EXAMPLE_TEST_NLL_MEAN: np.ndarray = np.array([
    0.6649315879552666,
    1.0259477340079999,
    0.05858235586832668,
    1.167316177883333,
    2.2621020254706665,
    0.28154766771606665,
    0.13721532084986668,
    0.02583790988095467,
    0.5150857351226666,
    0.4477122247134,
    0.8816752221246666,
    0.14262310741657333,
    0.6380860761773334,
    0.031145603274486667,
    0.09299362130605333,
    0.2171666349388,
    0.014538426943380927,
    0.8932884521737332,
    0.3236570966469333,
    0.1480420782719333,
    0.8324728021526665,
    0.10002357543412668,
    1.1268364260459998,
    1.3794413095873332,
    0.590553789006,
    0.07219779350588666,
    1.0746729727506665,
    0.46456549323133334,
    0.2518456821698667,
    0.6090546551660001,
    1.3435213473766667,
    0.2846124712678,
    0.09646769076967332,
    0.2580024846936,
    0.04010371851676801,
    0.17976529100314667,
    0.9259075442233333,
    1.0155291263739998,
    1.9345349450126665,
    0.19151373147624667,
    0.0284521378762,
    1.5789293027990001,
    0.1648644158243467,
    2.5100450999946666,
    0.9388649917654,
    0.2184470648048667,
    0.15719205766177333,
    0.3033292852574,
    0.2559612144097333,
    0.1474940772659,
    0.31355917029080665,
    0.08734176057770207,
    0.02867226936884266,
    0.028895894454509997,
    2.373496823740667,
    0.3230690574094,
    0.16836471168693334,
    0.41215556006466675,
    0.21884955275373338,
    0.007701757193630847,
    0.22149659771896674,
    0.05539630913923001,
    1.1509603610953332,
    1.4644726740953335,
    0.14342598820448665,
    0.7109785298808001,
    0.15198825998002666,
    0.17041944737599998,
    0.08742880257720001,
    0.0105705581395528,
    0.6634204006966666,
    0.3098186365165466,
    0.18708547567400663,
    0.3212144696813333,
    0.5262328185587999,
    1.2657481904193333,
    0.8029189901933333,
    0.16526893093353334,
    0.16181081395493335,
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    2.0498655610433336,
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    1.0062600927166665,
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    0.7342168033593333,
    1.787677865811333,
    0.7110020919860001,
    0.6701209893615333,
    0.12037305548079999,
    0.423743663535,
    0.2935553873640933,
    0.32429044630293336,
    0.041484121061513335,
    0.8081598203859999,
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    1.0458823911273334,
    0.45629573177199995,
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    0.42579296043646664,
    0.5926081337148,
    0.033970311326737994,
    1.9710357000106664,
    0.08818098307883335,
    2.0032275891011997,
    0.005190605080664846,
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    0.037563976271585336,
    0.7435474269481734,
    0.07989137127132667,
    0.3573821860552666,
    0.2783490285181333,
    1.829035036298,
    0.1817566776307067,
    0.08862872445657999,
    0.5136880449365999,
    1.5509986981946668,
    1.1702439360300667,
    0.5133044122754,
    0.5642295258154667,
    0.09677160094157333,
    0.5141772570035334,
    0.27974157338600003,
    0.8315857741959999,
    1.8874113732046667,
    0.017158170844306667,
    0.2776925280765333,
    0.2544694670127334,
    0.16941729700386668,
    0.35210577399340004,
    0.11018338967568934,
    0.014443012240174,
    0.039320578063646666,
    1.0359731487019999,
    0.1325327937044,
    0.00683044729364016,
    1.5009027267906667,
    0.1875084576016,
    0.7182476665622668,
    0.10411170563335999,
    0.07231705317608803,
    0.16976467322563998,
    0.23199750843026665,
    0.805546386432,
    0.21629968786856,
    0.28900721255699996,
    0.6598217193773467,
    0.06057574808738,
    0.11322126236162,
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    2.927029467608,
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    2.7182456437673337,
    1.283516748600667,
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    2.138874160876,
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    0.28969138891053337,
    0.12666888396308001,
    0.9479121727106667,
    0.5281677403102665,
    1.0281071967499997,
    0.06422649271986666,
    0.7695693632536,
    0.09722501018777534,
    0.04871285136699334,
    0.04694829038221,
    0.18759154175749335,
    1.040950387222067,
    0.24800083064893333,
    0.5968764270933999,
    0.01366289277753597,
    0.6465116967310001,
    0.3423751347646,
    0.70575616286,
    0.08232702340003334,
    0.1877132449616,
    0.2059927224748,
    2.2119141495713337,
    0.4723351372653333,
    2.5584955863826666,
    1.5445629870586668,
    0.048853967905879994,
    0.8548255245306666,
    0.48293059489519996,
    0.19038753822619997,
    0.1783527983296,
    0.3780650218256,
    0.1615259933294,
    0.7266572147273334,
    0.2432229202678,
    0.8157130402974667,
    0.1843742763562,
    0.1676986532648267,
    0.19433559895703065,
    0.5440125306944539,
    0.30293593902596,
    0.19117332666453332,
    0.49562019014146663,
    0.06799414119226,
    0.051656648679708005,
    0.6200513303948,
    1.2373639666029335,
    0.17109917207795336,
    1.2225775131573333,
    0.5359804778722668,
    0.3512370257231333,
    0.2003228888812667,
    0.17587788736786666,
    0.09836973859231066,
    0.18262656248686668,
    0.030247746144966465,
    0.2543474270828,
    0.14431768448840002,
    0.013866074030579267,
    1.4396737894566667,
    0.009025351895948924,
]).reshape((250,))

PER_EXAMPLE_TEST_NLL_MEAN_STANDARD_ERROR: np.ndarray = np.array([
    0.00016107382882696238,
    0.00018638409694371314,
    2.428522153262729e-05,
    0.00017604169067703517,
    0.0003611836909443064,
    7.560621837706488e-05,
    3.148595762605104e-05,
    1.148097085289879e-05,
    0.00014755844517447243,
    8.617469252804802e-05,
    0.00015925447666057926,
    4.4733678941492145e-05,
    0.00012123397342063928,
    1.5655519109619272e-05,
    2.7085527956018868e-05,
    5.5666415604367475e-05,
    1.4331328016143873e-05,
    0.0002171522966285733,
    9.116207035797389e-05,
    2.641966818137057e-05,
    0.00013568370910805135,
    2.6826049835857897e-05,
    0.00027216584818151617,
    0.00019335790151143287,
    0.00011271956044724986,
    2.4564402891454093e-05,
    0.0002203671212978582,
    7.37291334709686e-05,
    5.7884024691854156e-05,
    0.00013210995774909907,
    0.0002169280399802266,
    6.446777387593824e-05,
    3.0364935481491732e-05,
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    1.8209151768100147e-05,
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    0.00017807897773227432,
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    6.39925521471854e-05,
    1.6756982540017443e-05,
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    6.167871475248002e-05,
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    0.00023298219415802653,
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    5.810350607217753e-05,
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    5.706798551860028e-05,
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    8.809193264481379e-05,
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    0.00014568301826286214,
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    7.593133456956314e-05,
    5.130297985843903e-05,
    5.06296419420057e-05,
    4.481044945642688e-05,
    3.8802062377874566e-05,
    1.8733559936416944e-05,
    5.526804358852011e-05,
    3.878528151089324e-05,
    7.943045795242281e-06,
    0.00023547699092374546,
    1.000226650096468e-05,
]).reshape((250,))

PER_EXAMPLE_TEST_NLL_STANDARD_DEVIATION: np.ndarray = np.array([
    0.23364379148851144,
    0.26220074830527657,
    0.03342473189532062,
    0.24589316944735096,
    0.512870121865016,
    0.10582227663698203,
    0.046547804155282035,
    0.015893989432654725,
    0.2121913532754199,
    0.12714680963201175,
    0.23843779358059916,
    0.06309002174994757,
    0.17292639425614817,
    0.021299730960424913,
    0.03805674986073378,
    0.07976436662677729,
    0.01803627930901529,
    0.30060624740659214,
    0.13277202484280004,
    0.038645766063055795,
    0.20014740397486075,
    0.037920438450703225,
    0.38266488876473964,
    0.2899802847953772,
    0.16456928365899914,
    0.034522172317516885,
    0.314912849232739,
    0.10740919735371295,
    0.08470328486797866,
    0.1843093877741192,
    0.3046548346231429,
    0.09280953851135929,
    0.04283092657042771,
    0.1168208623380536,
    0.024608346925385445,
    0.11399740346990472,
    0.25389669616219823,
    0.25522821443815097,
    0.3541782226545793,
    0.09015588290584771,
    0.021888695109498302,
    0.7861800347465786,
    0.08317353836966469,
    0.7210988869299918,
    0.3178743187019249,
    0.08962186355740323,
    0.08089680371780632,
    0.0704496848893924,
    0.1141521605369464,
    0.07990004516213885,
    0.1580564352691401,
    0.07667979000003858,
    0.020011862152425994,
    0.015551361047150528,
    0.5117169860537352,
    0.12125291607827862,
    0.068410306299475,
    0.11623397573876967,
    0.08102223685990748,
    0.010648999165868938,
    0.11294028092157431,
    0.036949179763328135,
    0.211495870408466,
    0.27451336748105193,
    0.07974778929663986,
    0.23154277641124846,
    0.06711858937228263,
    0.06600240912340163,
    0.03725165764993671,
    0.00975841363734865,
    0.13804552708154635,
    0.16494066418625025,
    0.1047394436406605,
    0.10163963013328925,
    0.18497118729523382,
    0.18736155565317705,
    0.1326193419238664,
    0.06401934636991091,
    0.05658203073317727,
    0.04558446523972297,
    0.13914864421791767,
    0.007104315894753297,
    0.11636042219324043,
    0.17240561942531918,
    0.18847181208431363,
    0.01607067969558369,
    0.17810054456654018,
    0.0936501383495005,
    0.4291477416337928,
    0.15692400553156174,
    0.22095354973224324,
    0.4408208949044944,
    0.3560775678560624,
    0.23634008651255756,
    0.4071397804008239,
    0.18683408116430147,
    0.1965593506889293,
    0.03789316264023606,
    0.1303214861749457,
    0.1555277366735524,
    0.11192786055534443,
    0.023614257324566023,
    0.23229041072791806,
    0.06497699376071027,
    0.0974916676419841,
    0.055154535893142675,
    0.32259298595903513,
    0.21343749124205652,
    0.024103183334003832,
    0.42573540434104257,
    0.01456646501049141,
    0.14349424284125187,
    0.1197993936578623,
    0.010219496726401028,
    0.15977361110409158,
    0.15963574229896038,
    0.2283755298623153,
    0.023788879361763715,
    0.47187383508250036,
    0.03665316052496041,
    0.8058994083056662,
    0.005374896386673401,
    0.16535578203722395,
    0.02390213161485539,
    0.3811113751245941,
    0.04007034798699046,
    0.1702127669273331,
    0.08056257466609278,
    0.3381487086461462,
    0.09326053311149247,
    0.046320100967574926,
    0.22806132605103452,
    0.40071203180952225,
    0.41529212758427514,
    0.14239664925467027,
    0.1650944679135965,
    0.03558705904069902,
    0.1579821186820497,
    0.09989614780287888,
    0.23438896062261874,
    0.40704221500653687,
    0.012005887444570495,
    0.07873148285185264,
    0.09038588121269484,
    0.06005007641009773,
    0.08599370748815746,
    0.09169369924363974,
    0.009710042477581423,
    0.026932182315494456,
    0.18243177385426065,
    0.041435314590714444,
    0.007337242730065534,
    0.35881717138170677,
    0.05726385403273909,
    0.27863175310507793,
    0.05894774414441817,
    0.06280768720395483,
    0.08877277870585128,
    0.08081991502504503,
    0.2577935395902344,
    0.11082532329181427,
    0.114736334847545,
    0.3890080659359663,
    0.03207667085505674,
    0.06494738457456407,
    0.055289127669983874,
    0.12914588984197364,
    0.07659833169252302,
    0.9002635174711978,
    0.4560271634371441,
    0.7704922437294008,
    0.026891886021062773,
    0.12913076707431767,
    0.11916011790414738,
    0.033192001043689925,
    0.09491462690951893,
    0.5010585876253099,
    0.07944154952224429,
    0.24664479323461444,
    0.15696451140890338,
    0.1832818583222962,
    0.00917760473687467,
    0.1386773055747838,
    0.044936792007631676,
    0.1754496979379765,
    0.7598045112670546,
    0.3288321596683138,
    0.041945671487381354,
    0.45659237972023314,
    0.03332315317718168,
    0.10710889100293564,
    0.05633839151491993,
    0.239568023239154,
    0.16676568052262897,
    0.21361840480506733,
    0.02829765312268026,
    0.2492617129571469,
    0.06248036186293012,
    0.023665524921915454,
    0.03321611799797548,
    0.09176365658361282,
    0.49186949398979374,
    0.08031910596460898,
    0.1883858397531081,
    0.019579113972146504,
    0.19936292296410887,
    0.14822907872342247,
    0.16445351570285527,
    0.048867384915071706,
    0.07685958557713315,
    0.08509767260095076,
    0.4217272061931124,
    0.14861357700152833,
    0.4784584889915279,
    0.3032071308909477,
    0.022866475159449052,
    0.2181249657964064,
    0.17234505011288653,
    0.08802662369377524,
    0.07287084046598576,
    0.1343410576989722,
    0.059143056936114755,
    0.1851366582929854,
    0.08835536704312455,
    0.2712352642915945,
    0.07386152264271724,
    0.0722234659015984,
    0.14030774505353724,
    0.3319710573553739,
    0.16719447839840224,
    0.0660690492989056,
    0.21843019521319257,
    0.031105050620664258,
    0.032621778321813594,
    0.22314354184879753,
    0.6637834410218805,
    0.07267891128549704,
    0.24934877083494134,
    0.21019574936110136,
    0.10896970696861255,
    0.07183131538088446,
    0.07299247412663981,
    0.05965247260863128,
    0.05404908175557471,
    0.023974545911177127,
    0.07701632913765938,
    0.054334652889101484,
    0.010682596280287138,
    0.34710419978312906,
    0.012018888595735588,
]).reshape((250,))

TEST_NLL_MEAN: np.ndarray = np.array([
    136.62759244600002,
]).reshape(())

TEST_NLL_MEAN_STANDARD_ERROR: np.ndarray = np.array([
    0.003163103387155345,
]).reshape(())

TEST_NLL_STANDARD_DEVIATION: np.ndarray = np.array([
    4.00518841548168,
]).reshape(())
